Evaluating Dynamics in Affect Structure with Latent Markov Factor Analysis

In intensive longitudinal research, researchers typically consider the structure of affect to be stable across individuals and contexts. Based on an assumed theoretical structure (e.g., one bipolar or two separate positive and negative affect constructs), researchers create affect scores from items (e.g., sum or factor scores) and use them to examine the dynamics therein. However, researchers usually ignore that the affect structure itself is dynamic and varies across individuals and contexts. Understanding these dynamics provides valuable insights into individuals’ affective experiences. This study uses latent Markov factor analysis (LMFA) to study what affect structures underlie individuals’ responses, how individuals transition between structures, and whether their individual transition patterns differ. Moreover, we explore whether the intensity of negative events and the personality trait neuroticism relate to momentary transitions and individual differences in transition patterns, respectively. Applying LMFA to experience sampling data (N = 153; age: mean = 22; standard deviation = 7.1; range = 17 – 66), we identified two affect structures—one with three and one with four dimensions. The main difference was the presence of negative emotionality, and the affect dimensions became more inversely related when the affect structure included negative emotionality. Moreover, we identified three latent subgroups that differed in their transition patterns. Higher negative event intensity increased the probability of adopting an affect structure with negative emotionality. However, neuroticism was unrelated to subgroup membership. Summarized, we propose a way to incorporate contextual and individual differences in affect structure, contributing to advancing the theoretical basis of affect dynamics research.